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Ensuring global food security necessitates innovative solutions for early detection and precise classification of diseases in staple crops like rice. This study introduces an advanced approach for automated rice plant disease detection and classification by integrating deep learning and metaheuristic optimization techniques. Specifically, a deep dense neural network (DNN) is employed for its capacity to capture intricate patterns in images and extreme learning machine (ELM) for classification. To enhance the optimization process, an innovative variant of the Shuffled Shepherd Optimization (SSO) algorithm, known as Enhanced Artificial Shuffled Shepherd Optimization (EASSO), is introduced. EASSO incorporates adaptive strategies and enhanced exploration–exploitation mechanisms, enabling more efficient convergence during the training of the DNN. The proposed system operates by processing high‐resolution images of rice plants through the DNN, extracting nuanced features indicative of various diseases, including blast, bacterial blight, and brown spots. EASSO optimizes the DNN's parameters, maximizing its accuracy in disease classification. The synergy between DNN and EASSO ensures a robust and adaptive model capable of handling diverse and complex disease patterns. This automated approach significantly reduces the reliance on manual inspection, enabling timely intervention and improving overall agricultural productivity. Experimental results demonstrate the superiority of the DNN‐EASSO framework over traditional methods, showcasing higher accuracy rates and faster convergence. The incorporation of Enhanced Artificial Shuffled Shepherd Optimization enhances the precision and reliability of disease classification, making this integrated system a valuable tool for farmers and agricultural practitioners. This research represents a significant stride toward sustainable agriculture, showcasing the potential of advanced technologies in ensuring food security worldwide. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Inorganic Analytical Chemistry / Zhongguo Wuji Fenxi Huaxue is the property of Beijing Research Institute of Mining & Metallurgy Technology Group and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

To quickly obtain rice plant phenotypic traits, this study put forward the computational process of six rice phenotype features (e.g., crown diameter, perimeter of stem, plant height, surface area, volume, and projected leaf area) using terrestrial laser scanning (TLS) data, and proposed the extraction method for the tiller number of rice plants. Specifically, for the first time, we designed and developed an automated phenotype extraction tool for rice plants with a three-layer architecture based on the PyQt5 framework and Open3D library. The results show that the linear coefficients of determination (R2) between the measured values and the extracted values marked a better reliability among the selected four verification features. The root mean square error (RMSE) of crown diameter, perimeter of stem, and plant height is stable at the centimeter level, and that of the tiller number is as low as 1.63. The relative root mean squared error (RRMSE) of crown diameter, plant height, and tiller number stays within 10%, and that of perimeter of stem is 18.29%. In addition, the user-friendly automatic extraction tool can efficiently extract the phenotypic features of rice plant, and provide a convenient tool for quickly gaining phenotypic trait features of rice plant point clouds. However, the comparison and verification of phenotype feature extraction results supported by more rice plant sample data, as well as the improvement of accuracy algorithms, remain as the focus of our future research. The study can offer a reference for crop phenotype extraction using 3D point clouds. [ABSTRACT FROM AUTHOR]

Two kinds of grain, "millet, 粟米 sùmǐ" and "husked rice, 稻 dào", frequently appear in the Liye Qin Slips. Aside from these grains, another character seen in the Liye Qin Slips , nǎo, is thought to represent grain. It also represents the words for "brain, 腦 nǎo" in other excavated documents. Since the archaeological data show that rice cultivation was practised around the middle and lower Yangtze Valley, the homeland of Proto-Hmong Mien (formerly the state of Chu 楚地), the word for "rice plant, 稻 dào" seems to be a loanword from Proto-Hmong Mien *mbləu. The character nǎo is reconstructed as *nˤuʔ, which bears the same onset as the sound for "rice plant (or husked rice)" in North and East Hmongic languages nɯ (< *mbləu). Hence, we propose that the assimilation (*mbl- > *n-) in these languages could have occurred at the latest just before or after the Qin dynasty. [ABSTRACT FROM AUTHOR]

ABSTRACT Ensuring global food security necessitates innovative solutions for early detection and precise classification of diseases in staple crops like rice. This study introduces an advanced approach for automated rice plant disease detection and classification by integrating deep learning and metaheuristic optimization techniques. Specifically, a deep dense neural network (DNN) is employed for its capacity to capture intricate patterns in images and extreme learning machine (ELM) for classification. To enhance the optimization process, an innovative variant of the Shuffled Shepherd Optimization (SSO) algorithm, known as Enhanced Artificial Shuffled Shepherd Optimization (EASSO), is introduced. EASSO incorporates adaptive strategies and enhanced exploration–exploitation mechanisms, enabling more efficient convergence during the training of the DNN. The proposed system operates by processing high‐resolution images of rice plants through the DNN, extracting nuanced features indicative of various diseases, including blast, bacterial blight, and brown spots. EASSO optimizes the DNN's parameters, maximizing its accuracy in disease classification. The synergy between DNN and EASSO ensures a robust and adaptive model capable of handling diverse and complex disease patterns. This automated approach significantly reduces the reliance on manual inspection, enabling timely intervention and improving overall agricultural productivity. Experimental results demonstrate the superiority of the DNN‐EASSO framework over traditional methods, showcasing higher accuracy rates and faster convergence. The incorporation of Enhanced Artificial Shuffled Shepherd Optimization enhances the precision and reliability of disease classification, making this integrated system a valuable tool for farmers and agricultural practitioners. This research represents a significant stride toward sustainable agriculture, showcasing the potential of advanced technologies in ensuring food security worldwide.

One of the critical effects of climate change on the agricultural environment is drought stress. This study aimed to determine the growth, yield performance, and proline content of black rice local cultivars Bantul, Boyolali mutant M2 at various doses of gamma rays and the proline content of mutant M3 during drought stress. The experiment used a Completely Randomized Design (CRD) with the factor dose of gamma-ray irradiation (0 Gy, 100 Gy, 200 Gy, and 300 Gy), applied in both paddy fields and polybags to observe and compare the growth, yield, and proline content of black rice plants with the control treatment (0 Gy). Laboratory test results showed that Boyolali and Bantul cultivars irradiated at 100 Gy and 200 Gy, had the highest proline content, which increased under drought stress conditions in both cultivars. At 100 Gy, the average plant height and the number of productive tillers of black rice of Boyolali and Bantul cultivars were the highest, and the diversity of the two local cultivars at this dose was higher than at other irradiation doses. Applying 100 Gy and 200 Gy gamma-ray irradiation doses on the Boyolali cultivar significantly affected the panicle length character and the number of seeds per panicle. For the Bantul cultivar, panicle length and the number of seeds per panicle at an irradiation dose of 200 Gy showed significantly different results. The conclusion is that a reduced amount of gamma-ray irradiation can enhance the growth, yield, and proline content of M2 black rice

Monitoring rice plant growth is crucial for evaluating rice field management and yield production. RGB images are generated from Unmanned Aerial Vehicles (UAV) with RGB cameras. UAVs produce high spatial and temporal resolution, while RGB cameras are commonly used and cheap. The objectives of this study were to identify the spectral pattern of rice plant growth and to estimate yield production based on the spectral value of RGB images. The spectral pattern and yield estimation were analyzed using confidence interval (CI) and regression, respectively. Results show that spectral pattern during the vegetative until ripening stage forms a concave with minimum value in the generative stage and decreases towards the harvest stage. Based on the CI value, the high interval between upper and lower happened in the vegetative and ripening stages while the low interval happened in the generative stage. The high CI in the vegetative and ripening stages was due to the soil background and complexity of the rice plant canopy, respectively while the low CI in the generative stage was due to the homogeneous response of the leaf canopy. The best rice yield estimation based on the spectral value occurs in the ripening stage with an R² of 0.84. [ABSTRACT FROM AUTHOR]

Rice pest identification is essential in modern agriculture for the health of rice crops. As global rice consumption rises, yields and quality must be maintained. Various methodologies were employed to identify pests, encompassing sensor-based technologies, deep learning, and remote sensing models. Visual inspection by professionals and farmers remains essential, but integrating technology such as satellites, IoT-based sensors, and drones enhances efficiency and accuracy. A computer vision system processes images to detect pests automatically. It gives real-time data for proactive and targeted pest management. With this motive in mind, this research provides a novel farmland fertility algorithm with a deep learning-based automated rice pest detection and classification (FFADL-ARPDC) technique. The FFADLARPDC approach classifies rice pests from rice plant images. Before processing, FFADLARPDC removes noise and enhances contrast using bilateral filtering (BF). Additionally, rice crop images are processed using the NASNetLarge deep learning architecture to extract image features. The FFA is used for hyperparameter tweaking to optimise the model performance of the NASNetLarge, which aids in enhancing classification performance. Using an Elman recurrent neural network (ERNN), the model accurately categorises 14 types of pests. The FFADL-ARPDC approach is thoroughly evaluated using a benchmark dataset available in the public repository. With an accuracy of 97.58, the FFADL-ARPDC model exceeds existing pest detection methods. [ABSTRACT FROM AUTHOR]

Rice is an important staple food crop and a primary energy source for more than half of the world's population. One of the deadly fungal diseases of rice produced by Magnaporthe oryzae is known as "blast disease." It has consistently threatened the world's food supply. This study aimed to dissect the complex blast resistance mechanism of the rice plant against blast pathogens. To understand the molecular mechanisms of blast resistance, we analyzed transcriptome changes in rice cultivars. Using transcriptome data obtained from an RNA-seq analysis of two samples comprising resistant (BR2655) and susceptible (HR12) genotypes under control and stress conditions was carried out. The total RNA sequencing generated a dataset of four samples, representing more than 40 Gb fastq raw data. A total of 145 million filtered reads were generated after the quality check. High-quality clean reads mapped rice reference genome RGAP 7. These aligned reads were taken for further analysis. A total of 11,867 DEGs across the genotypes under stress were identified. A total of 7577 and 4290 genes showed significant differential expression in BRC vs. BRIN library and HRC vs. HRIN library, respectively. The results reveal that differential gene expression analysis clearly shows the changes in the expression profiling of BR2655 and HR12 cultivars. [ABSTRACT FROM AUTHOR]

Finding a method, which may indicate the contribution of soil parameters including organic matter, pH and clay to pretilachlor leaching (persistence) in the soil, with the use of a suitable indicator, which indicates pretilachlor presence in the soil is of significance. Accordingly, using undisturbed soil columns, four paddy fields (A, B, C, D) in the suburbs of Babol city (Mazandaran province, northern Iran) were sampled before the preparation and irrigation of the fields in April 2021. Soil samples were transferred into PVC pipes (divided into 2 cm layers) measuring 12 (height) × 10 cm (diameter), and were injected with pretilachlor at the recommended (1.75 L/ha) and high doses (3.5 L/ha). The surface layers of all fields had a higher concentration of pretilachlor and organic matter with the highest contribution to pretilachlor persistence followed by clay and pH. In the 0–4 cm depth, herbicide concertation was the lowest in field A (139 mg/kg) and the highest in field C (161 mg/kg). The corresponding values for organic matter were equal to 1.88 and 5.68%, respectively. The bioassay of rice (the indicator plant), with a significant correlation with chemical analysis results, indicated field A and field C had the pretilachlor infiltration of 6 and 4 cm, respectively. Accordingly, rice is a suitable plant indicator for measuring the presence of pretilachlor as examining its shoot length is a good criterion for bioassay. Additionally, changes in the amount of organic matter in different soil layers can be used to predict the leaching level of pretilachlor. [ABSTRACT FROM AUTHOR]

To quickly obtain rice plant phenotypic traits, this study put forward the computational process of six rice phenotype features (e.g., crown diameter, perimeter of stem, plant height, surface area, volume, and projected leaf area) using terrestrial laser scanning (TLS) data, and proposed the extraction method for the tiller number of rice plants. Specifically, for the first time, we designed and developed an automated phenotype extraction tool for rice plants with a three-layer architecture based on the PyQt5 framework and Open3D library. The results show that the linear coefficients of determination (R2) between the measured values and the extracted values marked a better reliability among the selected four verification features. The root mean square error (RMSE) of crown diameter, perimeter of stem, and plant height is stable at the centimeter level, and that of the tiller number is as low as 1.63. The relative root mean squared error (RRMSE) of crown diameter, plant height, and tiller number stays within 10%, and that of perimeter of stem is 18.29%. In addition, the user-friendly automatic extraction tool can efficiently extract the phenotypic features of rice plant, and provide a convenient tool for quickly gaining phenotypic trait features of rice plant point clouds. However, the comparison and verification of phenotype feature extraction results supported by more rice plant sample data, as well as the improvement of accuracy algorithms, remain as the focus of our future research. The study can offer a reference for crop phenotype extraction using 3D point clouds.

As silicon is known to have a positive role in enhancing the resistance of rice plants to insects, an investigation on one aspect of the biochemical and molecular basis of rice plant defences mediated by silicon amendments against brown plant hopper Nilaparvata lugens (Stål), comprising field, pot culture and laboratory experiments was undertaken in OUAT. Two organic products; Diatomaceous Earth (DAE) at 0.15, 0.30 and 0.45 t/ha, and Rice Hull Ash (RHA) at 2, 3, 4 t/ha, along with one inorganic source, calcium silicate (CaSiO3) at 2, 3, 4 t/ha were soil applied as basal to evaluate their effects on the accumulation of silicon, proline, phenol, carbohydrates and protein in the plant tissues along with proteomic and Scanning Electron Microscope (SEM) studies. Results showed decreasing in proline and protein contents and increasing in silicon, phenol and carbohydrates contents in infected Si amended plants as compared to the control. Proteomic study showed appearing of a thick band of about 20 KDa in infested plants indicating its role in defense mechanism. Under SEM, the dumbbell shaped deposits of Si were marked clearly at different doses of silicon, indicating that the increase in silica dose enhanced its deposits, which was supported by EDAX-SEM data. Results of this study clearly demonstrated that soil amendments with silicon through organic and inorganic sources effectively caused biochemical and molecular changes that ultimately support the plant defenses against BPH. [ABSTRACT FROM AUTHOR]

Since the micro-level presence of iron is essential in plant overall development, iron is considered a crucial trace element and serves a leading role in the farming sector globally. Its lack in the plant disrupts the homeostasis mechanism. Biofortification is a new method of obtaining crops with increased levels of trace elements in edible sections for food consumption. Drought is considered an important problem and farmers are extremely worried. This is a global issue that has been increased and sustained for an extended period due to an insufficient supply of regular water to meet the needs. Biochar is a carbon-rich substance produced by pyrolysis of biomass with a limited oxygen supply. [ABSTRACT FROM AUTHOR]

Rice tends to accumulate heavy metals present in soil that have been introduced by human activities and pass them up the food chain. The present study aimed to evaluate the accumulation of selected trace elements (Cu, Zn, and Pb) in paddy and soil and the transfer of these metals from soil to rice by analysing the bioconcentration factor (BCF), bioaccumulation factor (BAF), and translocation factor (TF) of heavy metals in paddy (Oryza sativa L.) and soil. Samples of matured paddy and the substrates were collected from three different areas located near a rural point (RP), a transportation point (TP), and an industrial point (IP). Heavy metal concentrations present in the soil and various parts of the plants were ascertained using an atomic absorption spectrophotometer (AAS). Cu, Zn, and Pb accumulation in the soil were detected in increasing orders of RP > TP > IP, IP > TP > RP, and IP > RP > TP, respectively. The BCFshoot, BAF, and transfer factor of both Zn and Pb from soil to rice were detected in the order of TP > IP > RP, which was different from Cu, where BCFshoot and TF showed the order of RP > IP > TP but the BAF indicated IP > RP > TP. TF > 1 was discovered for Zn and Pb at the TP, and for Cu at the RP, which could be attributed to the TP's strongly acidic soil and Cu's abundance in the RP's soil. Paddy height and yield traits were the most significant at the IP site, showing the highest number of fertile spikelets, the average weight of a 1000-paddy spikelet, and the harvest index (0.56). These findings can be related to the normal range of Zn and Pb found in rice plants that support growth. Thus, the findings of this study demonstrated that soil properties and metal abundance in soil from certain land use practices can partially influence the mobility and transfer of metals through soil–plant pathways. [ABSTRACT FROM AUTHOR]

Ferromanganese oxide biochar composite (FMBC) is an efficient remediation material for cadmium -contaminated soils. However, the effect of FMBC under varied water managements on the remediation of Cd-polluted soil is unclear. In this study, we conducted both incubation and field experiments to investigate the combined effects of corn-stover-derived biochar modified with ferromanganese on the immobilization and uptake of Cd by rice under continuous aerobic (A), aerobic-flooded (AF), and flooded-aerobic (FA) water management regimes. The results showed that loading iron-manganese significantly increased the maximum sorption capacity (Qm) of Cd on FMBC (50.46 mg g−1) due to increased surface area, as compared to the pristine biochar (BC, 31.36 mg g−1). The results revealed that soil Eh and pH were significantly affected by FMBC and it’s synergistic application with different water regimes, thus causing significant differences in the concentrations of DTPA-extractable Cd under different treatments. The lowest DTPA-extractable Cd content (0.28–0.46 mg−1) was observed in the treatment with FMBC (2.5 %) combined FA water amendment, which reduced the content of available Cd in soil by 2.63–28.4 %. Moreover, the treatments with FMBC-FA resulted the proportion of residual Cd increased by 22.2 % compared to the control. Variations in the content and fraction of Cd had a significant influence on its accumulation in the rice grains. The FMBC-FA treatments reduced the Cd concentration in roots, shoots and grains by 37.97 %, 33.98 %, and 53.66 %, respectively, when compared with the control. Predominantly because of the reduction in Cd biological toxicity and the improved soil nutrient content, the combined application increased the biomass and yield of rice to some extent. Taken together, the combination of the Fe-Mn modified biochar and flooded-aerobic water management may potentially be applied in Cd-polluted soil to mitigate the impacts of Cd on rice production.

Copyright of Journal of Agricultural Chemistry & Biotechnology is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Jurnal Ilmu Komputer dan Agri-Informatika is the property of IPB University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Eighteen pesticide-degrading endophytic bacteria were isolated from the roots, stems, and leaves of healthy rice plants and identified through 16S rRNA gene sequencing. Furthermore, biochemical properties, including enzyme production, dye degradation, anti-bacterial activities, plant-growth-promoting traits, including N-fixation, P-solubilization, auxin production, and ACC-deaminase activities of these naturally occurring endophytic bacteria along with their four consortia, were characterized. Enterobacter cloacae HSTU-ABk39 and Enterobacter sp. HSTU-ABk36 displayed inhibition zones of 41.5 ± 1.5 mm, and 29 ± 09 mm against multidrug-resistant human pathogenic bacteria Staphylococcus aureus and Staphylococcus epidermidis, respectively. FT-IR analysis revealed that all eighteen isolates were able to degrade chlorpyrifos pesticide. Our study confirms that pesticide-degrading endophytic bacteria from rice plants play a key role in enhancing plant growth. Notably, rice plants grown in pots containing reduced urea (30%) mixed with either endophytic bacterial consortium-1, consortium-2, consortium-3, or consortia-4 demonstrated an increase of 17.3%, 38.6%, 18.2%, and 39.1% yields, respectively, compared to the control plants grown in pots containing 100% fertilizer. GC–MS/MS analysis confirmed that consortia treatment caused the degradation of chlorpyrifos into different non-toxic metabolites, including 2-Hydroxy-3,5,6 trichloropyridine, Diethyl methane phosphonate, Phorate sulfoxide, and Carbonochloridic. Thus, these isolates could be deployed as bio-stimulants to improve crop production by creating a sustainable biological system. [ABSTRACT FROM AUTHOR]

Technological help can be used for improving the cultivation of critical crops for optimal production and quality. Automatic plant disease detection is an interesting study issue as it may be beneficial for the monitoring of vast agricultural fields and thus the automatic identification of disease by the symptoms in the various sections of plants. This work contributes an automated diagnosis of different rice-related diseases utilizing image processing, deep learning, machine learning, and methods for meta-heuristic optimization. These measures include picture dataset size, class numbers, preprocessing procedures, classification approaches, performance analysis, etc. Researches from the previous decade are extensively reviewed, including studies on numerous rice plant diseases, and an investigation of the key features is provided. The survey provides insights into the various approaches used to identify disease in rice plants. Different attributes evaluated for the study include the kind of segmentation, dividing technology, extracted features, author name, dataset size and year of publication, disease category, techniques, accuracy of detection as well as classification and constraints. Furthermore, a model using a hybrid deep learning technique is proposed to identify diseases in rice plant such as rice blast, brown spots, leaf smut, tungsten and sheath. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Agricultural Chemistry & Biotechnology is the property of Egyptian National Agricultural Library (ENAL) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Drought is one of the main abiotic stresses responsible for reducing crop yields worldwide. In hybrid rice production, cytoplasmic male sterility (CMS) displays an alternative plan for producing high-yielding hybrid rice depending on the hybrid vigor. Kinetin (Kin) has an essential role during the early phase of grain setting by regulating cell division, assimilate flow, and osmotic modification under water deficit. Growth, floral, and yield-related traits were evaluated in two CMS lines under five irrigation intervals and two concentrations of the phytohormone kinetin. Our study was conducted to explore the effects of irrigation intervals (continuous flooding (CF), irrigation every six (I6), nine (I9), twelve (I12), and fifteen (I15) days, and kinetin exogenously applied (control, 15 mg L−1, and 30 mg L−1) on growth, floral, and yield-related traits. Growth traits (i.e., number of leaves (NOL), days to complete leaf number (DCLN), days to heading (DTH, 50%), flag leaf area (FLA), and plant height (PH)), floral traits (i.e., duration of spikelet opening (DSO), panicle length (PL), spikelet opening angle (SOA), and panicle exertion (PE)), and seed yield traits (i.e., seed set (SS), panicle weight (PW), seed yield (SY), harvest index (HI), and number of fertile panicles per hill (NFP)) in two CMS lines. Implementation of kinetin displayed the highest significant and positive values for all growth, floral, and yield-related traits when compared to the control (average of applied irrigation intervals). Applying 30 mg L−1 kinetin positively enhanced the growth traits (i.e., NOL, FLA, and PH with 4.1%, 5%, and 3%, respectively), floral traits (i.e., PL, PE, SOA, and DSO with 5.4, 5.7, 5.9, and 5.4%, respectively), and yield-related traits (i.e., PW, SS, SY, HI, and NFP with 22%, 17%, 14%, 14.5%, and 15%, with the same sequence) compared to non-treated plants. Consequently, exogenous foliar spray of kinetin could be an effective process in minimizing the harmful effects (the reduction in PW, SS, SY, HI, and NFP recorded 41%, 61%, 45%, 30%, and 48%, respectively, under I15 conditions when compared to CF) of water deficit in hybrid rice and increasing seed production. [ABSTRACT FROM AUTHOR]

Soil amendments have been extensively employed for the purpose of remediating soils contaminated with cadmium (Cd). However, the potential impacts of soil amendments on soil chemical properties, soil Cd bioavailability, total Cd accumulation by rice, and rhizosphere bacterial community in Cd-contaminated paddy fields located in a tropical region is still at its infancy. In this study, a commercial MgO-CaO-SiO2 conditioner (A), biochar (B), and a combination of the commercial MgO-CaO-SiO2 conditioner and biochar with a ratio of 1:1 (C) were applied at two different doses [2250 kg ha−1 (A150, B150, C150), 4500 kg ha−1 (A300, B300, C300)] to investigate their impacts on soil Cd stabilization and total Cd uptake of rice straw and grain in a Cd-contaminated remediation field experiment. Rhizosphere bacterial community diversity and composition were also assessed using high-throughput sequencing based on 16S rRNA genes. Compared with non-amendment treatment (CK), soil pH, cation exchange capacity (CEC), organic matter (OM), total nitrogen (TN), available nitrogen (AN), and nitrate (NO3−) concentrations were significantly elevated, whereas ammonium (NH4+) and soil available Cd concentrations were reduced by soil amendment treatments. Meanwhile, soil amendments significantly decreased concentrations of total Cd in both rice straw and grain, with the lowest Cd concentration in the C300 treatment. Soil pH and CEC were significantly and negatively associated with soil Cd availability and rice straw and grain Cd concentrations, while NH4+ concentration was positively correlated with soil available Cd concentration, and OM, TN, and NO3− concentrations were positively linked with rice grain Cd concentration. Soil amendments significantly increased bacterial Chao 1 and Shannon indexes and altered bacterial community composition in rhizosphere soil, due to changes in the composition of the community primarily influenced by variations in soil pH, CEC, and soil available Cd, NH4+, available phosphorous (AP) and available Potassium (AK) concentrations. Furthermore, the abundant bacterial species (Pseudomonas) and rare bacterial species (Bacillus, Candidatus_Solibacter and Streptomyces) have been up-regulated by different soil amendments, which might be in favour of soil Cd immobilization. A structural equation model also showed that soil amendments could improve bacterial diversity and change soil pH and CEC, which were conducive to hindering the removal and conversion of Cd. Overall, these results indicate that biochar-(MgO-CaO-SiO2) mixed amendments at high dosage exerted better performance compared with single application soil amendment A and B. The changes in soil chemical properties, available Cd content, and rhizosphere bacterial community assembly induced by soil amendments are closely correlated with the decrease in rice’s ability to accumulate Cd.

The earlier diagnosis and classification of plant diseases has the ability to control the spread of illnesses on a variety of crops with the aim of improving crop quality and yield. The automatic system effectively recognizes the plant diseases at less error and cost without the interpretation of farm specialists. In this article, shuffled shepherd social optimization-based deep learning (SSSO-based deep learning) technique is developed to classify rice leaf disease and severity percentage prediction. The classification is carried out using deep maxout network and the severity percentage prediction is performed using deep LSTM. The training of both deep learning techniques is achieved using developed SSSO algorithm, which is the combination of shuffled shepherd optimization algorithm (SSOA) and social optimization algorithm. The proposed technique achieved maximum accuracy, sensitivity, specificity of 0.926, 0.935, 0.892, and minimum mean square error, and root mean square error of 0.106, and 0.326. The accuracy of the implemented approach is 7.24%, 5.29%, 4%, and 2.81% improved than the existing techniques, like bacterial leaf streak-based UNet (BLSNet), multilayer maxout, resistance spot welding-based deep recurrent neural network (RSW-based deep RNN), and rider Henry gas solubility optimization_deep neuro fuzzy network (RHGSO_DNFN)+deep LSTM. [ABSTRACT FROM AUTHOR]

MobileNet Convolutional Neural Network is a predominately used Deep Learning (DL) model in various computer/mobile vision applications. However, it is clear from the literature that the researches using MobileNet Convolution Neural Network (CNN) reported with only minimal level of classification accuracy in the case of plant nutrient deficiency classification. As a result, on the benchmark rice plant dataset, a modified MobileNet CNN architecture is proposed in this study for nutrient deficiency classification. Insertion of additional new layers, a system to handle dropout and selecting appropriate optimizer are among the alterations included in this proposed method to increase the model performance accuracy. Subsequently, the performance of the modified MobileNet CNN model is evaluated using well-defined performance metrics such as accuracy, F 1 -score, precision, and recall. In addition, the performance of the proposed modified MobileNet CNN model is compared with the state-of-the-art conventional DL models such as VGG16, VGG19, ResNet152, ResNet152V2, ResNet101, ResNet101V2, ResNet50, ResNet50V2, InceptionV3, InceptionResNetV2, MobileNet, and MobileNetV2. Empirical evaluation of the proposed model with the cutting-edge DL models shows that the modified MobileNet CNN model outperforms the aforementioned DL models. Finally, we have developed a web and mobile application for automatically classifying nutrient deficiency in rice plants that the farmers could easily use. [ABSTRACT FROM AUTHOR]

Aphelenchoides besseyi Christie (Nematoda: Aphelenchoididae) is a seed-borne ectoparasite of Oryza sativa rice and causes 'white tip' disease. When seeds are soaked in water, the nematodes emerge from them, arrive at rice plants by swimming in flooded paddy fields, and reproduce on them. Two separate experiments were conducted to determine the relationships of the nematode abundance in water to the arrival (infection) and subsequent multiplication of nematodes, the disease incidence, and the yield of rice grains. Experiment 1 showed that, as the A. besseyi population (Pi) surrounding the seedlings increased, both the disease incidence of plants and the number of nematodes (Pf) in all seeds harvested per plant increased, but the rate of increase in the nematode population (Pf/Pi) decreased. Negative relationships were found between the Pi level and the estimated probability of nematode arrival, although it was extremely difficult to isolate all arrivals from plants (experiments 2 − 5). However, the number of arrivals was estimated to increase with the Pi level. It is considered from these results that the density-dependent decrease in the rate of increase (Pf/Pi) was determined largely by the density-dependent reduction in the rates of nematode establishment (colonization) and/or post-establishment reproduction on plants. [ABSTRACT FROM AUTHOR]

The aim of this study was to develop an efficient bioinoculant for amelioration of adverse effects from chilling stress (10°C), which are frequently occurred during rice seedling stage. Seed germination bioassay under chilling condition with rice (Oryza sativa L.) cv. Tainan 11 was performed to screen for plant growth-promoting (PGP) bacteria among 41 chilling-tolerant rice endophytes. And several agronomic traits were used to evaluate the effects of bacterial inoculation on rice seedling, which were experienced for 7-d chilling stress in walk-in growth chamber. The field trials were further used to verify the performance of potential PGP endophytes on rice growth. A total of three endophytes with multiple PGP traits were obtained. It was demonstrated that Pseudomonas sp. CC-LS37 inoculation led to 18% increase of maximal efficiency of Photosystem II (PSII) after 7-d chilling stress and 7% increase of chlorophyll a content, and 64% decline of malondialdehyde content in shoot after 10-d recovery at normal temperature in walk-in growth chamber. In field trial, biopriming of seeds with strain CC-LS37 caused rice plants to increase shoot chlorophyll soil plant analysis development values (by 2.9% and 2.5%, respectively) and tiller number (both by 61%) under natural climate and chilling stress during the end of tillering stage, afterward 30% more grain yield was achieved. In conclusion, strain CC-LS37 exerted its function in increase of tiller number of chilling stress-treated rice seedlings via improvement of photosynthetic characteristics, which in turn increases the rice grain yield. This study also proposed multiple indices used in the screening of potential endophytes for conferring chilling tolerance of rice plants. [ABSTRACT FROM AUTHOR]

To clarify the risk of sulfur deficiency in rice plants, available sulfur and heavy metals in paddy soil at 100 points in the Aichi Prefecture were quantified. By comparing the measurement results with existing provisional evaluation methods, some areas were found to be at risk of developing rice sulfur deficiency. It has been suggested that there is a latent occurrence of sulfur deficiency in paddy rice in the Aichi Prefecture; therefore, we investigate d the paddy fields near areas where the risk of occurrence was considered to be high. We analyzed the growth of paddy rice, the sulfur content in the foliage, and the yield of unpolished rice, and observed no sulfur deficiency. Based on the above results, areas with risk of occurrence of sulfur deficiency in paddy rice were found in Aichi Prefecture based on the provisional evaluation method, but no deficiency was observed in the surveyed areas. In the future, closer attention should be given to the occurrence of paddy rice sulfur deficiency in areas with low available sulfur content in soils of the Aichi Prefecture. [ABSTRACT FROM AUTHOR]

Sustainable agricultural practices are required, especially for irrigation, to provide for a growing population. About 85% of the freshwater resources in the world are used for irrigation. ''Us,'' current irrigation procedures must be either updated or replaced with cutting-edge, intelligent systems that utilize the ML, IoT (Internet of Things), and sensor networks which are wireless. In the proposed paper, a brainy system for tracking and scheduling accuracy irrigation using IoT, a LoRa-based machine learning (ML) is being introduced. We developed an independent irrigation technique that would provide the tomato and eggplant with the precise amount of water they required was set up using the information obtained from the soil moisture sensors. In comparison to traditional watering, which required 7541 mL for the banana plant and 8755 mL for the rice plant, the irrigation system irrigated the plants with an overall quantity of 5010.745 mL & 4421.09 mL in one month. Overall, this led to a 46% reduction in water usage, and the plants looked better than they would have with conventional watering. The simulation results clearly show that the suggested approach uses water more sensibly than cutting-edge models in the experiment farming region.

Introduction: Correct assessment of evapotranspiration fluctuations in different meteorological scenarios plays an important role in the optimal management of water resources. Probability analyzes with different probabilities of occurrence can increase flexibility in decision making and increase the reliability of decisions. Rice (Oryza sativa L.) is one of the most important agricultural products in the world. Although rice is cultivated in a wide range of climatic and geographical conditions, it is vulnerable to changes in environmental conditions. Planting management, design of irrigation systems, and suitable irrigation cycle for optimal production are important issues for sustainable production. Materials and Methods: The study area includes the northern region of Iran, i.e. the provinces of Gilan, Mazandaran and Golestan, which is the main rice-growing area in Iran. Changes in rice evapotranspiration in three different cultivation dates with four different occurrence probabilities of 75, 50, 25 and 10%, was calculated using the FAO Penman-Monteith equation and meteorological data with a statistical period of 30 years (2020- 1990). Also, the average rice crop coefficient at different stages of growth in 10-day periods was estimated based on the Weibull model. These probabilities represent the probable limits of the expected values of evapotranspiration in different scenarios of low, normal, high, and very high evapotranspiration years. Results and Discussion: The results showed a relatively constant difference of 1 to 2 mm between different rice cultivation histories in the major rice cultivation areas of Gilan and Mazandaran in normal to very high evapotranspiration years. In the years of low evapotranspiration, the water requirement was significantly different from the normal, high and very high evapotranspiration years, which decreased from east to west. This difference was approximately 30% higher in Golestan province as compared with other areas. In the early planting situation relative to the late planting situation in the major western and central coastal areas, there was a 10% decrease in water consumption. At the scale of the whole growing season in Gorgan, evapotranspiration in different conditions of planting date was on average 20% (1300 cubic meters) more than the main regions of Gilan and Mazandaran. In case of timely planting, the net irrigation requirement in very high evapotranspiration years was about 2000 cubic meters per hectare more than the normal years. In years with high evapotranspiration, late planting increased the net irrigation requirement by more than 210 mm compared to different planting dates in Gorgan. According to the obtained results, the largest difference between evapotranspiration values during normal and very high evapotranspiration years was in the late planting situation. Therefore, it seems that late planting causes a significant increase in water consumption in the high evapotranspiration years. Consequently, it is better to avoid rice cultivation when the rice growing season is anticipated to be warm. Conclusion: Evapotranspiration, as one of the main components of the hydrological cycle, had a significant role in proper irrigation planning and water resources management. The results underline the importance of estimating the rice evapotranspiration to avoid appreciable yield loss under extreme conditions.

Red chili is one of the plants that are widely cultivated in Indonesia because of its high economic value. However, chili production in Indonesia has not been able to meet the needs. The low productivity of chili is caused by the growth of spoilage microorganisms along with the physiological changes of the product during post-harvest. This study aims to determine the effect of concentration and duration of beeswax immersion on the shelf life of chili (Capsicum annum L.) with the addition of galangal for anthracnose resistance. This research was conducted at the Horticulture Laboratory, Department of Agrotechnology, Faculty of Agriculture, Syiah Kuala University Darussalam Banda Aceh from January to February 2022. This study used a Factorial Completely Randomized Design (CRD). The first factor is the concentration of beeswax with the addition of 10% galangal extract at each concentration and the second factor is the length of immersion of beeswax, with 3 replications. The results showed that giving beeswax at a concentration of 4% and 6% with the addition of galangal extract could maintain weight loss, moisture content, vitamin C, intensity of disease attack and had the highest value on organoleptic tests on scent. [ABSTRACT FROM AUTHOR]

The productivity of rice plants in swamps can be increased by adding dolomite lime. The addition of dolomite lime can reduce soil acidity. This study aims to determine the increase in productivity of rice plants in swamps with the addition of dolomite lime. The research was conducted from September to December 2021 at the Sekap Bumi Farmer's Group, Padang Tambak Village, Pino District - South Bengkulu. This study uses a package of rice cultivation in swampland Treatment 1 land added 500 kg of dolomite lime and treatment 2 did not add dolomite lime. Data analysis was done descriptively. The data collected is the productivity of rice yields in each treatment. The results showed that the application of dolomite lime can increase rice productivity in swamps by about 10%. Therefore, farmers are advised to provide dolomite lime when cultivating rice in swamps. [ABSTRACT FROM AUTHOR]

In the Asian continent rice cultivation process provide staple food for livelihood. A current research work in the agriculture area involves recognizing and classifying plants diseases based on live images. Farmer can traditionally do the cultivation process, hence here the identification of the disease was by manual (visual appearance) or send the sample data set to the nearest laboratory. In our proposed method we will provide accurate and early detection of various diseases in Oryza sativa (rice) plants, that can help the farmers in applying suitable treatment on the rice plants and improve productivity. We are using optimized deep learning models such as the ResNet-152, CoAtNet for classification and identify the diseases. We have captured healthy and unhealthy images from Villupuram district, Tamil Nadu, India. The total amount of captured images was 3071 from our farmer's field with proper sunlight. It was highly efficient and detects the diseases or recognizes the diseases from the captured image with different categories (Bacterial Leaf Blight, Leaf Blast, Brown Spot, and Tungro / Leaf smut). The experimental results show according to the proposed method CoAtNet, was achieved for overall achieved accuracy of 96.56%. [ABSTRACT FROM AUTHOR]

The Reference Transcriptomic Dataset (RTD) is an accurate and comprehensive collection of transcripts originating from a given organism. It holds the key to precise transcript quantification and downstream analysis of differential expressions and regulations. Currently, transcriptome annotations for most crop plants are far from complete. For example, Oryza sativa indica (O. sativa indica) is reported to have 40,759 transcripts in the Ensembl database without alternative transcript isoforms and alternative splicing (AS) events. To generate a high-quality RTD, we conducted RNA sequencing of rice leaf samples collected at various time points during Rhizoctonia solani infection. The obtained reads were analyzed by adopting the recently developed computational analysis pipeline to assemble the RTD with increased transcript and AS diversity for O. sativa indica (IndicaRTD). After stringent quality filtering, the newly constructed transcriptome annotation was comprised of 122,968 nonredundant transcripts from 53,695 genes. This study identified many novel transcripts compared to Ensembl deposited data that are important for regulating molecular and physiological processes in the plant system. Currently, the assembled IndicaRTD must allow fast quantification of transcript and gene expression with high precision. [ABSTRACT FROM AUTHOR]

An in situ model paddy field experiment was conducted using non-contaminated soil to identify factors that locally increase the radiocesium activity concentrations in rice near the water inlet of paddy fields. The results demonstrated that irrigation water intake reduced exchangeable potassium concentration and increased radiocesium activity concentration in the soil near the water inlet; thus, the radiocesium activity concentration in rice was negatively correlated with the former and positively with the latter. Exchangeable potassium flushing and deposition of suspended solids in the soil owing to irrigation water intake facilitated the transfer of radiocesium to rice plants near the water inlet. [ABSTRACT FROM AUTHOR]

This study examines the genome sequences of five endophytic bacterial isolates from the Oryza sativa microbiome to assess their potential as plant bio-inoculants. The five complete bacterial genomes from the genera Pseudomonas, Burkholderia, Sphingobacterium, Stenotrophomonas, and Pantoea were sequenced using Nanopore long-read sequencing technology.

The Reference Transcriptomic Dataset (RTD) is an accurate and comprehensive collection of transcripts originating from a given organism. It holds the key to precise transcript quantification and downstream analysis of differential expressions and regulations. Currently, transcriptome annotations for most crop plants are far from complete. For example, Oryza sativa indica (O. sativa indica) is reported to have 40,759 transcripts in the Ensembl database without alternative transcript isoforms and alternative splicing (AS) events. To generate a high-quality RTD, we conducted RNA sequencing of rice leaf samples collected at various time points during Rhizoctonia solani infection. The obtained reads were analyzed by adopting the recently developed computational analysis pipeline to assemble the RTD with increased transcript and AS diversity for O. sativa indica (IndicaRTD). After stringent quality filtering, the newly constructed transcriptome annotation was comprised of 122,968 non-redundant transcripts from 53,695 genes. This study identified many novel transcripts compared to Ensembl deposited data that are important for regulating molecular and physiological processes in the plant system. Currently, the assembled IndicaRTD must allow fast quantification of transcript and gene expression with high precision.

Eighteen pesticide-degrading endophytic bacteria were isolated from the roots, stems, and leaves of healthy rice plants and identified through 16S rRNA gene sequencing. Furthermore, biochemical properties, including enzyme production, dye degradation, anti-bacterial activities, plant-growth-promoting traits, including N-fixation, P-solubilization, auxin production, and ACC-deaminase activities of these naturally occurring endophytic bacteria along with their four consortia, were characterized. Enterobacter cloacae HSTU-ABk39 and Enterobacter sp. HSTU-ABk36 displayed inhibition zones of 41.5 ± 1.5 mm, and 29 ± 09 mm against multidrug-resistant human pathogenic bacteria Staphylococcus aureus and Staphylococcus epidermidis, respectively. FT-IR analysis revealed that all eighteen isolates were able to degrade chlorpyrifos pesticide. Our study confirms that pesticide-degrading endophytic bacteria from rice plants play a key role in enhancing plant growth. Notably, rice plants grown in pots containing reduced urea (30%) mixed with either endophytic bacterial consortium-1, consortium-2, consortium-3, or consortia-4 demonstrated an increase of 17.3%, 38.6%, 18.2%, and 39.1% yields, respectively, compared to the control plants grown in pots containing 100% fertilizer. GC–MS/MS analysis confirmed that consortia treatment caused the degradation of chlorpyrifos into different non-toxic metabolites, including 2-Hydroxy-3,5,6 trichloropyridine, Diethyl methane phosphonate, Phorate sulfoxide, and Carbonochloridic. Thus, these isolates could be deployed as bio-stimulants to improve crop production by creating a sustainable biological system.

Phenylpropanoid biosynthesis plays crucial roles in the adaptation to cadmium (Cd) stress. Nevertheless, few reports have dabbled in physiological mechanisms of such super pathway regulating Cd accumulation in plants. Herein, by integrating transcriptomic, histological and molecular biology approaches, the present study dedicated to clarify molecular mechanism on how rice adapt to Cd stress via phenylpropanoid biosynthesis. Our analysis identified that the enhancement of phenylpropanoid biosynthesis was as a key response to Cd stress. Intriguingly, POD occupied a significant part in this process, with the number of POD related genes accounted for 26/29 of all upregulated genes in phenylpropanoid biosynthesis. We further used SHAM (salicylhydroxamic acid, the POD inhibitor) to validate that POD exhibited a negative correlation with the Cd accumulation in rice tissues, and proposed two intrinsic molecular mechanisms on POD in contributing to Cd detoxification. One strategy was that POD promoted the formation of lignin and CSs both in endodermis and exodermis for intercepting Cd influx. In detail, inhibited POD induced by external addition of SHAM decreased the content of lignin by 50.98–66.65 % and delayed percentage of the D TIP-CS to root length by 39.17–104.51 %. The other strategy was expression of transporter genes involved in Cd uptake, including OsIRT1 , OsIRT2 , OsZIP1 and OsZIP , negatively regulated by POD. In a word, our findings firstly draws a direct link between POD activity and the Cd accumulation, which is imperative for the breeding of rice with low-Cd-accumulating capacity in the future. [Display omitted] • 26 of 29 upregulated genes in phenylpropanoid biosynthesis are associated with POD. • POD negatively regulate Cd uptake and accumulation. • POD involve in promoting the formation of lignin and CSs in endodermis and exodermis for Cd detoxification. • POD involve in orchestrating the membranal metal transporters on Cd detoxification. [ABSTRACT FROM AUTHOR]

Excessive chemical spraying can lead to waste, damage rice plants, and reduce yield. To resolve this problem, the precise spraying of interplant areas through targeting methods was investigated in this study. The touch-sensing data for identification and positioning was extracted by the trigger rule of binocular diffuse reflection sensors. Wavelet transform decomposition is used to extract time-domain and frequency-domain features from identification data. These combined features build a rice plant identification model based on support vector machine. The model could determine whether the light sensing system was triggered by rice plants or weed. Positioning data was divided into several regions based on the light sensing system. Then, maximum voltage was extracted from the positioning regions. The relative position of the rice plants was determined by fitting previous coordinates and maximum values. Finally, the nozzle position was corrected by the lateral movement of robot arms to achieve the precise targeted spraying. Field performance tests showed that the targeted intermittent spraying had 12.78% and 17.66% less spraying coverage rates in the non-targeted root and interline areas, respectively, and 21.9% more spraying coverage rates in the targeted interplant areas. The overall spraying dosages were 7.81 and 9.41 L for the targeted intermittent and non-targeted continuous spraying, respectively; the spraying dosage saved was 18%. The targeting application system proposed in this paper can achieve a significant reduction in chemical dosage. This research results provide a practical method for reduced herbicide application in rice paddies. • Tactile sensing method is resistant to complex backgrounds in paddy fields. • The sensor in contact with the stalk achieves rice positioning directly. • Active target application tackles the issue of non-linear seedling belt. [ABSTRACT FROM AUTHOR]

Nitrogen is indispensible ingredient of animal food and comes initially from plant. In fact, plant converts the nitrogen of soil in two forms suitable for nutrition of animals. After water, nitrogen is the second limiting factor for plant growth in many fields and deficiency of this element is met by fertilizers. The excessive use of chemical fertilizer has generated several environmental issues. These problems can be tackled by use of Bio-fertilizers. Bio-fertilizers are different from Chemical fertilizers. Bio-fertilizer on the application remains in soils, multiply and keep benefiting the growing crops. Blue green algae (BGA) or Cyanobacteria, the free-living photosynthetic N-fixing organism, are distributed worldwide and contribute to the soil fertility in many agricultural ecosystem. Cyanobacteria function as a Bio-fertilizer due to the property of nitrogen fixation (conversion of molecular nitrogen into nitrogen compound). The mass production of Cyanobacteria is much cheaper and easy than that of conventional chemical fertilizer and their inorganic requirement is very simple. To test the efficacy of nitrogenous fertilizer along with BGA on the yield attributes of rice, a field experiment was conducted with rice variety Pusa 1509 and Pusa sharbati. Two fields of 2.0 m into 3.0 m were prepared one without BGA as control and other with BGA. For this field experiment recommended dose of BGA is 10 kg/ha after the transplantation of rice seedlings. Inoculates contains the mixture of Gloeocapsa, Plectonema, Oscillatoria, Anabaena, Nostoc, Cylindrospermum, Scytonema, Calothrix, Anabaenopsis, Aulosira, Eisherella etc. As compared with control tillers were seen after 80 days, food grains were thick, elongated and healthy and 10-30 percent grain yield found to be increased over control. The observation clearly indicates that the BGA can be the good option to increase the growth and crop production in Rice. [ABSTRACT FROM AUTHOR]

Weeds are found on every cropland across the world. Weeds compete for light, water, and nutrients with attractive plants, introduce illnesses or viruses, and attract harmful insects and pests, resulting in yield loss. New weed detection technologies have been developed in recent years to increase weed detection speed and accuracy, resolving the contradiction between the goals of enhancing soil health and achieving sufficient weed control for profitable farming. In recent years, a variety of platforms, such as satellites, airplanes, unmanned aerial vehicles (UAVs), and close-range platforms, have become more commonly available for gathering hyperspectral images with varying spatial, temporal, and spectral resolutions. Plants must be divided into crops and weeds based on their species for successful weed detection. Therefore, hyperspectral image categorization also has become popular since the development of hyperspectral image technology. Unmanned aerial vehicle (UAV) hyperspectral imaging techniques have recently emerged as a valuable tool in agricultural remote sensing, with tremendous promise for weed detection and species separation. Hence, this paper will review the weeds problem in rice fields in Malaysia and focus on the application of hyperspectral remote sensing imagery (HRSI) for weed detection with algorithms and modelling employed for weeds discrimination analysis. [ABSTRACT FROM AUTHOR]

Salt stress hinders plant growth and disrupts many physio-biochemical and molecular processes. The present study evaluated the potential role of foliar applied 24-epibrassinolide (EBR) for mitigating the adverse effects of salinity in rice plants. Our findings showed that salt stress reduced several growth and physiological parameters, and substantially increased oxidative stress in rice seedlings. The foliar application of EBR replenished the negative effects of salt stress by improving plant growth and physiological attributes in rice plants. Moreover, application of EBR substantially reduced oxidative injuries by enhancing antioxidant enzymes and phenolic compounds in rice plants during salt stress. Interestingly, the endogenous ABA levels were declined in stressed plants by EBR treatment, also validated by expression analysis of ABA-related genes. In addition, application of EBR upregulated carotenoids and flavonoids pathway genes, thus showing its function to activate secondary metabolism in rice plants during salt stress. The principal component analysis (PCA) indicated that, under salt stress, the response of EBR-treated rice plants was strongly correlated with the accumulation of phenolic compounds and flavonoids. Collectively, this study presents useful insight into the distinct role of EBR by activating underlying enzymatic and non-enzymatic defense mechanisms to mitigate salinity in rice plants.

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Nitrogen fertilizers are widely applied to increase rice yields, but excessive fertilization poses an environmental risk. It has been shown that rice-duck farming can be more efficient in terms of N use by improving rice growth. Several pathways have been proposed for how ducks may improve rice growth in paddy fields. The aim of this study was to investigate the isolated effect of rice-duck farm soil on rice, namely the N content in different plant organs and whether it differs among rice varieties. In a 116-day greenhouse pot experiment, six different Japonica rice varieties (JinU99, Jinyuan98, Jindao18, Jinyuan89, Jinhei1 and Jindao201) were grown in fertilized duck and no-duck soil after which the N content in their organs and the numbers of surviving and grain-producing plants were compared. The straw and leaf N concentrations were positively influenced by rice-duck farm soil while in the roots, this effect was recorded in only two rice varieties. Grain N content differed among varieties, but was not significantly influenced by soil type. Overall, N concentrations in straw and leaf, and roots of some rice varieties, but not in grain grown on duck soil were higher than that in those grown in soils not influenced by rice-duck farming. This study for the first time demonstrates that rice-duck farm soil alone can influence rice growth, namely an increase in the N content of certain rice plant organs. [ABSTRACT FROM AUTHOR]

Salt stress hinders plant growth and disrupts many physio-biochemical and molecular processes. The present study evaluated the potential role of foliar applied 24-epibrassinolide (EBR) for mitigating the adverse effects of salinity in rice plants. Our findings showed that salt stress reduced several growth and physiological parameters, and substantially increased oxidative stress in rice seedlings. The foliar application of EBR replenished the negative effects of salt stress by improving plant growth and physiological attributes in rice plants. Moreover, application of EBR substantially reduced oxidative injuries by enhancing antioxidant enzymes and phenolic compounds in rice plants during salt stress. Interestingly, the endogenous ABA levels were declined in stressed plants by EBR treatment, also validated by expression analysis of ABA-related genes. In addition, application of EBR upregulated carotenoids and flavonoids pathway genes, thus showing its function to activate secondary metabolism in rice plants during salt stress. The principal component analysis (PCA) indicated that, under salt stress, the response of EBR-treated rice plants was strongly correlated with the accumulation of phenolic compounds and flavonoids. Collectively, this study presents useful insight into the distinct role of EBR by activating underlying enzymatic and non-enzymatic defense mechanisms to mitigate salinity in rice plants. [ABSTRACT FROM AUTHOR]

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